The term “changes in structure,” originating from work in econometrics, refers to structural modifications invoked by actions on a causal model. In this paper we formalize the representation of reversibility of a mechanism in order to support modeling of changes in structure in systems that contain reversible mechanisms. Causal models built on our formalization can answer two new types of queri...

Acknowledgements I would like to give special thanks to my advisor, Professor Kevin Hoover who took my shortcomings and successes with equal stride, and who struggled through the process alongside me. But how will you look for something when you don't in the least know what it is? How on earth are you going to set up something you don't know as the object of your search? To put it another way, ...

This paper describes the theoretical underpinnings and architecture of a new type of learning environment that incorporates features of microworlds and of intelligent tutoring systems. The environment is based on a progression of increasingly sophisticated causal models that simulate domain phenomena, generate explanations, and serve as student models. Constraints on model evolution are discuss...

The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same bias-reducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e. satisfy the back-door criterion) or (2) the Markov boundaries surrounding the treatment variable are identical in both sets. We...

Instrumental variables (IVs) are widely used to identify causal effects. For this purpose IVs have to be exogenous, i.e., causally unrelated to all variables in the model except the explanatory variable X . It can be hard to find such variables. A generalized IV method has been proposed that only requires exogeneity conditional on a set of covariates. This leads to a wider choice of potential I...

STATISTICAL ANALYSIS IN EMPIRICAL BAYES AND IN CAUSAL INFERENCE

of the Dissertation Complete Identification Methods for Causal Inference

The paper addresses uncertain reasoning on a causal model given by two layered networks, where nodes in one layer express possible causes and those in the other are possible e$ects. Uncertainty of causalities is expressed in a novel manner, i.e. by Conditional Causal Possibilities. The expression has two advantages over the conventional way with conditional possibilities: it expresses the exact...

The central aims of this experiment were to compare observational and interventional learning of a simple causal chain, and to ascertain whether people represent their interventions in accordance with the normative model proposed by Pearl (2000). In the observation condition people treated putative causes as independent, and systematically selected the wrong model. In the intervention condition...